Energy and Cost Efficient Data Centers
Baris Aksanli

Citation
Baris Aksanli. "Energy and Cost Efficient Data Centers". PhD thesis, University of California San Diego, June, 2015.

Abstract
Data centers need efficient energy management mechanisms to reduce their consumption, energy costs and the resulting negative grid and environmental effects. Many of the state of the art mechanisms come with performance overhead, which may lead to service level agreement violations and reduce the quality of service. This thesis proposes novel methods that meet quality of service targets while decreasing energy costs and peak power of data centers. We leverage short term prediction of green energy as a part of our novel data center job scheduler to significantly increase the green energy efficiency and job throughput. We extend this analysis to distributed data centers connected with a backbone network. As a part of this work, we devise a green energy aware routing algorithm for the network, thus reducing its carbon footprint. Consumption during peak periods is an important issue for data centers due to its high cost. Peak shaving allows data centers to increase their computational capacity without exceeding a given power budget. We leverage battery-based solutions because they incur no performance overhead. We first show that when using an idealized battery model, peak shaving benefits can be overestimated by 3.35x. We then present a distributed control mechanism for a more realistic battery system that achieves 10x lower communication overhead than the centralized solution. We also demonstrate a new battery placement architecture that outperforms existing designs with better peak shaving and battery lifetime, and doubles the savings. Data centers are also good candidates for providing ancillary services in the power markets due to their large power consumption and flexibility. This thesis develops a framework that explores the feasibility of data center participation in these markets, focusing specifically on regulation services. We use a battery-based design to not only help by providing ancillary services, but to also limit peak power costs without any workload performance degradation.

Electronic downloads

Internal. This publication has been marked by the author for TerraSwarm-only distribution, so electronic downloads are not available without logging in.

Citation formats

• HTML

  Baris Aksanli. <a
  href="http://www.terraswarm.org/pubs/606.html"
  ><i>Energy and Cost Efficient Data
  Centers</i></a>, PhD thesis,  University of
  California San Diego, June, 2015.

• Plain text

  Baris Aksanli. "Energy and Cost Efficient Data
  Centers". PhD thesis,  University of California San
  Diego, June, 2015.

• BibTeX

  @phdthesis{Aksanli15_EnergyCostEfficientDataCenters,
      author = {Baris Aksanli},
      title = {Energy and Cost Efficient Data Centers},
      school = {University of California San Diego},
      month = {June},
      year = {2015},
      abstract = {Data centers need efficient energy management
                mechanisms to reduce their consumption, energy
                costs and the resulting negative grid and
                environmental effects. Many of the state of the
                art mechanisms come with performance overhead,
                which may lead to service level agreement
                violations and reduce the quality of service. This
                thesis proposes novel methods that meet quality of
                service targets while decreasing energy costs and
                peak power of data centers. We leverage short term
                prediction of green energy as a part of our novel
                data center job scheduler to significantly
                increase the green energy efficiency and job
                throughput. We extend this analysis to distributed
                data centers connected with a backbone network. As
                a part of this work, we devise a green energy
                aware routing algorithm for the network, thus
                reducing its carbon footprint. Consumption during
                peak periods is an important issue for data
                centers due to its high cost. Peak shaving allows
                data centers to increase their computational
                capacity without exceeding a given power budget.
                We leverage battery-based solutions because they
                incur no performance overhead. We first show that
                when using an idealized battery model, peak
                shaving benefits can be overestimated by 3.35x. We
                then present a distributed control mechanism for a
                more realistic battery system that achieves 10x
                lower communication overhead than the centralized
                solution. We also demonstrate a new battery
                placement architecture that outperforms existing
                designs with better peak shaving and battery
                lifetime, and doubles the savings. Data centers
                are also good candidates for providing ancillary
                services in the power markets due to their large
                power consumption and flexibility. This thesis
                develops a framework that explores the feasibility
                of data center participation in these markets,
                focusing specifically on regulation services. We
                use a battery-based design to not only help by
                providing ancillary services, but to also limit
                peak power costs without any workload performance
                degradation.},
      URL = {http://terraswarm.org/pubs/606.html}
  }
  

Posted by Baris Aksanli on 14 Aug 2015.